1. Field of the Invention
Definition
The word “Machine” as it is used throughout these specifications is intended to mean a computer with a life expectancy of five to ten years—including an operating system or platform (ex. Mac or PC) that may be incompatible with other systems or platforms, various shared and specialized software with a life expectancy of one to three years, and an internet connection equal to current DSL or Broadband. The word “Machines” as it is used in this specification is intended to mean advanced networks of machines that change and improve over one person, research group, or entire field of study's lifetime.
The invention specifically relates to search, time dependent data compilation and user controlled display methods. The systems and methods described herein will clarify the roles of human conceptual and creative abilities versus the computational skills of machines and corresponds to the fields of Artificial Intelligence (AI); Knowledge Management (KM); Human Computer Interaction (HCI); Coded Data Generation, Processing and Conversion; Horology; Acoustic and Image Analysis; Measuring and Testing; and Dynamic Information Storage and Retrieval. The systems and methods described herein will lead to the ultimate compression and feature extraction algorithm.
The systems and methods described herein are a human computer interaction process using individual and collaborative human cognitive abilities, memories, aesthetics, preferences, knowledge, and conceptual integration skills to arrange, index and record data relationships using advanced networks of machines. Relationships among data and data arrangements are measured by machines and perceived by people as: evolving configurations of data in groups over time; scalable character-like symbols that refer and place each component within each configuration; and multidimensional hierarchical waveforms composed of light, sound and other machine derived data display techniques to distribute and compare overall data arrangements and characteristics before the data itself is retrieved from the original collection.
Context Driven Topologies (CDTs) are continually invented and reinvented through use. Precisely matching versions may not be observed in real life or in real machines, however, overall relationships captured by the topologies are commonly understood without special training or programming.
The systems and methods described herein create a level of abstraction and simplification for the search, comparison and analysis of complex, evolving data collections. The changing records are virtual, time dependent and measured for comparison, presence, location, traces and signs using non-linear dynamics, knot theory topology, algebra, Fourier analysis and other mathematical techniques. The most appropriate mathematical measurements vary by purpose and may include frequency, proportion, density, distance, relative degree of rotation, similarities and variations in alignment or intensity and other specific techniques contained in the “knowledge patterns”.
Supplemental technical specifications for the future technologies claimed herein, partially disclosed throughout these specifications, and prototyped through an upcoming project [FIGS. 6A-6C] include, but are not limited to:
Mathematical templates/patterns for masking and redundancy elimination; special focusing, fine tuning, resolution, intensity, color, texture, phase and polarization techniques; controls (e.g. switching, gating or modulating) to modify and adjust the direction and orientation of light, sound and other derived data waveforms arriving from independent and concurrent sources. The sources vary in number, physical location and era of time, therefore, are always fixed relatively to the origins of each query and transmission. Variations due to this relativity are corrected, streamlined or otherwise made consistent for particular uses through the use of the mathematical patterns themselves. Each pattern is constructed for a different reason, uses its own measures, has its own similarities and will therefore encounter and reconcile each variation its own way.
Context Driven Topologies are constructed to show data and data relationships as they are periodically recorded, as they change over time, as they are interpreted with different knowledge, and as they are interpreted from different points of view. Each topology can be demodulated to reflect these views and changes through a process using techniques similar to harmony and discord, or blending and contrast, to break information into smaller groups and components. Likewise, new groups are created to simplify, remove, consolidate, blend or merge components, smaller groups and topologies to be perceived as one new component, group or Context Driven Topology.
New pattern constructions and modulation techniques may be initiated by an individual, a society or research group, one computational machine or network of machines (9.13)\.
The mathematical patterns and/or their modulations may be transferred locally or globally using the methods disclosed to expand or compress the space the patterns and forms are perceived to be in by changing the frequency of light, sound and other encoded logic elements as they are processed and displayed by devices and systems specially controlled by individual or networked users to investigate and interpret data and data relationships for specific reasons.
The systems and methods described herein are used to obtain and interpret records using waves that in some cases are other than optical waves. The systems and methods described herein comprise a dynamic, shared memory (Section 8) using image and other specific data arrangements as records.
Context Driven Topologies are broadcast to be distributed in the waveform state, similar to existing radio or cell phone technology and initially ‘powered’ simply by being propagated through use, similar to language, songs, stories and information on the internet. Special compilers, broadcasting, retrieval and presentation equipment will be developed in the future. See paragraph (1.24) regarding electrical pulses.
The inventor is an independent curator who organizes museum content and collections by selecting, categorizing, numbering, indexing, describing and presenting objects in meaningful hierarchies to tell cultural, scientific and historical stories through physically designed spaces, objects, voices, projection geometries and immersive environments that simulate a feeling of ‘being there’.
The invention was prompted in 2001 during research on thermodynamics for Shanghai Scienceland in China. Influences include a series of readings in physics, mathematics, new physics, quantum mechanics, chemistry, biology, light and optics, acoustics, philosophy; and a continuous dialogue discussing the merits of various learning interactives listed in Chinese and legible only by their numbers, then the design of these same interactives using as little natural language as possible to avoid the intricacies of multiple translations. This was followed by the International Spy Museum which included concepts of encryption, encoding, revealing/concealing, and piecing together a puzzle from the “partially seen” and “partially true”. This project lacked one clear direction or voice, the content included constantly updating artifacts, stories, architectures, spaces, environments, programs and scopes of work documented through matrices, specifications, photographs and CAD drawings. Each of these influences were added together to lead to the systems and methods described herein.
2. Description of the Prior Art
Mathematics: the systems and methods described herein are a new application of Graph Theory; Knot Theory Topology; Algebra, Group Theory, Combinatorics, Fourier Analysis, and various interrelationships between these fields and other pure or applied prior art that is most clearly expressed and understood through mathematics.
Artificial Intelligence (AI), Knowledge Management (KM), Human Computer Interaction (HCI): CDTs particularly address subject matters related to mapping; complex indexing of events, objects and agents; parallel processing; data mining and privacy; user directed interface; hierarchical structures; sequence and flow in comparison processes; new forms of node representation and topologies; visualization and simulation; a new system and theory of computational linguistics and process grammars; mechanisms for shared memory; machine learning and training; design; scalable data and networks; automatic updating; compression and decompression; techniques for data curation, interpretation and preservation; pattern, shape, motif and object generation, identification and recognition; text, visual, audio and other machine derived representations of encoded information; unsupervised clustering; techniques for the interpretation of partially described data and data relationships; illustrative embodiments; containers, wrappers and boundaries; parsing; traces; new abilities for machines to generalize, associate and categorize; selection methods; rules; heuristics; priority registry and addressing; periodicities; thresholds; infinite variables; redundancy and masking; custom consistency and similarity measures; error and irregularity detection; new types of I/O devices, methodologies and purposes; an improved process for metadata, determining order, partial order and concepts of matching; machine implementation and simulation of human intelligence, decision making, and conceptual integration; the directed use of language, memory, imagery, sounds and encoding for specific purposes. The systems and methods described herein give machines “something to measure” that is closer to our imagination, cultures, changing interpretations, and historical comprehension. Context Driven Topologies are used to compile, generate and present results a new way. They are a better form of metadata that easily scales and a marked departure from tree structures, or other standard data arrangements, because the topologies provide a new way for information to characterize, organize and identify itself in context over time.
Physics, Quantum Mechanics, Astronomy, Chemistry, Biology and other Sciences: the need to measure; our quest to discover, diagnose, explore, and evaluate; logic; problem solving and accuracy; fundamental relationships; simplicity and complexity; elegance; the desire for robust, rigorous, precise investigations based on solid foundations with the intention of leading to significant, new proofs and conclusions; and, our basic human relationship with time, nature and understanding of forms and processes.
Cognitive Science, Ontological Engineering and Semiotics: symbols; language; translation; word meaning; history; schemas, foundations and rationale; metaphor and representation; our need to communicate across cultures and generations; our need to share information, record and discuss.
Art and Music: aesthetics; composition; clarity; simplification; abstraction; layering; similarities and patterns, returning to the same; unique variations and interpretations; reflections of cultures; questions about conventions and our societies; perception; awareness; preference; and the need to express.
Architecture and Design: drawing methods, perspective and rendering for discussion versus schedules, plans, elevations, details, sections and overalls for building; careful attention to proportion, and relationships between adjacent spaces; lighting and acoustics; material properties including durability, compatibility, texture and color.
Statement on Prior Art versus the Invention: Similar patented subject matters identify or create information object types, properties, subsets of properties, data characteristics and arrange information units into ordered sequences or relationships, however outside of Classes 706 and 707, very few subjects even nominally address what the information itself means, why it was generated, the reality that some information is more important or lasting than other information, and how this influences peoples interpretation of these graphs, patterns, objects, properties and characteristics. Nor does most prior art allow for these properties and characteristics to evolve, be influenced, and recorded over time. Generally, prior art is based on a delicate balance between the ways data relationships are described and derived but does not allow data descriptions or derivations to vary by preference or specific quality assurances, and how these preferences and assurances affect the value of data. Very generally, objects and experiences, such as artworks and scientific studies, reflect or attempt to capture what is genuine and the process of curation, interpretation, and preservation of data generated to represent these objects and experiences needs to aim for a virtual connection that is as direct as possible between maker and viewer, nature and observer. The systems and methods described herein are intended for information that has had, at least at one time, a profound attachment to the original user(s). The systems and methods described herein are a tool to let these attachments become more obvious and are based on the belief that a more thorough understanding of context will not only ensure more meaningful and direct connections in the future, but that use of the systems and methods described herein will dramatically increase our abilities to consolidate and manage shared long term data resources of higher quality and value, which is also barely, if ever, addressed in similar patents and subject matters reviewed in prior art.
A project has been organized in collaboration with individual theorists, mathematicians, artists, engineers, and other inventors to: look carefully at the reasons and purposes for the methods and systems described herein from a variety of view points; to generate a representative dataset [FIGS. 6A-6C]; to develop an enhanced prototype that is a mathematical, visual and audio model, and new conceptual framework; to create and define the first set of knowledge patterns, display patterns, memory forms, measurable arcs and to further demonstrate and clarify the techniques described herein using a sampler set of ideas that reference these individual's methods of constructing ideas, and the ways these ideas are manifest through art, science, engineering and language. See the detailed description of [FIGS. 6A-6C] for an explanation of this process.
This particular project, which may or may not be in collaboration with an established US research partner such as a public or private university, museum, research institute, or information technology company, will be called “Digitizing the Non Digital”, “Visualization of Context Driven Topologies” “Inside/Outside” or other name. This collaborative project will be proposed to US Federal, private and international agencies along with research partners identified as legitimate collaborators in these proposals. Future research partners may be from the United States or foreign countries.
The systems and methods described herein will be introduced and partially implemented with a variety of US and international individuals and institutions to assess the invention's compatibility with specifically varying resources. It will be proposed that the work of this project be presented and discussed at US and international art museums, research institutes, conferences, universities and other places and events in an effort to disseminate the ideas and methodology of the systems and methods described herein; gather feedback from a variety of cultures; form lasting partnerships with these individuals and institutions to use the systems and methods described herein on larger, broader and more specific collections of complex and abstract information. The more widely the systems and methods described herein are used, the more useful they will be. The purpose of the project is to establish quality controls and a firm foundation for future technologies so use of the systems and methods described herein is not confusing, geared to one domain or culture over another, related too strongly to natural language, or current machine processing, indexing, computation and display methods.